test.py

import streamlit as st
from dotenv import load_dotenv
from crew.til import TilCrew
from streamlit_extras.capture import stdout
load_dotenv()



def main():
    st.markdown("<div class='container'>", unsafe_allow_html=True)

    st.markdown(
        """
        <div class="centered">
            <p class="title">Today I Learnt Feedback</p>
            <p class="description">Feedback on Today I Learnt</p>
        </div>
        """,
        unsafe_allow_html=True
    )
    til_content = st.text_area('Enter what you learnt today:',
                               "I went through the following course on quantization of LLM: https://www.deeplearning.ai/short-courses/quantization-in-depth/ and here are my insights: \n"
                               "* Quantization is the process of reducing the size of LLM models by reducing the underlying weights.\n"
                               "* The weights are reduced by scaling down the datatypes from a datatype that takes larger space to a data type that takes a smaller space, this is also known as downcasting.\n"
                               "* Advantages: takes lesser space and increases compute speed\n"
                               "* Disadvantages: Answers are less precise\n\n"
                               "My notes on Synthetic data:\n"
                               "* Why is it needed? Product Testing, Training ML Algos, Reduced constraints when using reglated data.\n"
                               "* Types: Fully Synthetic data, Partially Synthetic data\n"
                               "* Varities: Text, Tabular, Media.\n"
                               "* Benchmarks to consider: Accuracy, Privacy\n"
                               "* Techniques: Statistical distribution, Agent to model, Using Deep Learning/Generative AI, Synthetic Minority Oversampling Technique.",
                               key='til_content', help='Enter what you learnt today')

    if st.button("Get Feedback"):
        with st.status(
            "🤖 **Analysing TIL...**", state="running", expanded=True
        ) as status:
            with st.container(height=500, border=False):
                log_container = st.empty()
                with stdout(log_container.code, terminator=""):
                    feedback = TilCrew()
                    inputs = {"content": til_content}
                    results = feedback.kickoff(inputs=inputs)["feedback"]
            status.update(
                label="✅ Feedback ready!",
                state="complete",
                expanded=False,
            )

        for result in results:
            st.markdown(f"#### TIL: {result['til']}")
            st.markdown(f"**Feedback:** {result['feedback']}")
            if result['feedback'] == "not_ok":
                st.markdown(f"**Criteria:** {result['feedback_criteria']}")
                st.markdown(f"**Reason:** {result['reason']}")
                if result.get('suggestion') is not None:
                    st.markdown(f"**Suggestion:** {result['suggestion']}")


if __name__ == "__main__":
    main()